Grey Markov Model Prediction Method for Regular Pedestrian Movement Trend

Fang, Xinsheng; Li, Xiaobin; Yu, Tao; Guo, Zhen; Ma, Tao

doi:10.1007/978-981-13-2288-4_55

Cited by 4 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e relative value is calculated using the original and gray forecast data and is divided into m intervals according to the relative value [20], and each interval represents the current state. Since too many intervals will cause data complexity and less divisions will not allow accurate data prediction, so the interval is generally divided into 3-4, using S i ∈ [l i , b i ] means i � 1, 2, 3, .…”

Section: State Divisionmentioning

confidence: 99%

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

Shen

Ren

2021

Security and Communication Networks

View full text Add to dashboard Cite

With the deepening of globalization, cross-border industrial e-commerce has increasingly become a mainstream means of trade exchanges, however, with the continuous increase in business volume. How to accurately estimate the procurement volume of cross-border materials is a significant issue, which plays a vital role in quantifying economic efficiency. To this end, this study proposes a financial decision-making algorithm for cross-border industrial e-commerce material procurement based on the Markov chain. The algorithm first preprocesses the original logistics data, including data cleaning, missing data filling, and noisy data removal, represents the data as structured panel data, finally builds a Markov chain model based on the panel data, and then makes predictions on the new data. We verified the effectiveness of the proposed model on a simulated dataset and an actual dataset.

show abstract

Section: State Divisionmentioning

confidence: 99%

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

Shen

Ren

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The above model prediction methods require accurate construction of motion models, which were difficult to describe the motion of pedestrians in more complex and diverse scenarios. Regarding machine models to predict trajectories, Fang et al [26] used a grey prediction algorithm to build Markov models of regular pedestrian data to predict the trajectories of pedestrians walking based on common pedestrian motion trends. The above trajectory prediction methods require hand-designed features and a clear understanding of the various behavioural movements of pedestrians.…”

Section: Pedestrian Trajectory Predictionmentioning

confidence: 99%

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Zhou,

Bai,

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

With the development of deep learning technology, the problem of data‐driven trajectory prediction and intention recognition has been widely studied. However, the pedestrian trajectory prediction and intention recognition methods based solely on data‐driven have weak data description ability and black‐box characteristics, which cannot reason about pedestrian crossing intention and predict pedestrian crossing trajectory as humans do. To address the above problems, the authors proposed a data and knowledge‐driven pedestrian intention estimation and trajectory prediction method by imitating human cognitive mechanisms. In the pedestrian intention inference process, the authors adopted the knowledge‐driven method. As a first step, the authors built a knowledge graph of pedestrian crossing scenes, and then paired it with a Bayesian network to estimate pedestrian crossing intentions. In the pedestrian trajectory prediction process, the authors used a data‐driven approach, combining pedestrian crossing trajectory features and knowledge‐based pedestrian intentions. Experiments show that all evaluation metrics of pedestrian trajectory prediction were improved after adding pedestrian intentions obtained by knowledge‐driven.

show abstract

“…The prediction of pedestrian behaviour is becoming an increasingly popular research field (Ridel, Rehder, Lauer, Stiller, & Wolf, 2018). It is assessed using different general and more sophisticated methods known in traffic planning theory (Fang, Li, Yu, Guo, & Ma, 2019;Hartmann, Ferrara, & Watzenig, 2018;Hartmann, Stolz, & Watzenig, 2018;Particke, Hiller, Feist, & Thielecke, 2018;Wu, Ruenz, & Althoff, 2018). The pedestrian movement, however, is less predictive than motorised traffic.…”

Section: Perceived Benefits From Adding New Pedestrian Bridges To Eximentioning

confidence: 99%

Location Preferences of New Pedestrian Bridges Based on Multi-Criteria Decision-Making and GIS-Based Estimation

Zagorskas

Turskis

2020

BJRBE

View full text Add to dashboard Cite

Non-motorized pedestrian and bicycle traffic is an effective and efficient tool for reducing the negative environmental impacts of transport and improving the quality of life in urban conditions. The strategies of creating new attractive spaces on the waterfront are prevalent amongst the municipalities in different countries. This kind of development intends the construction of new connection bridges, usually meant solely for walking and cycling. There are a significant number of studies covering the theme of pedestrian bridges, but the studies typically focus on technical parameters – serviceability, stress and vibrations, specifications for the design. Researchers and stakeholders rarely discuss displacement strategy, expenditure and future usability. This study aims to find out the best and the most useful bridge locations that would contribute to pedestrian network improvement, would add value to city image and give other benefits. A novel hybrid Multi-Criteria Decision-Making (MCDM) model, based on five different multi-criteria decision-making methods: Multiplicative Exponential Weighting (MEW), method of Evaluation Based on Distance from Average Solution (EDAS), an Additive Ratio Assessment (ARAS) method, expert judgement, and Step‐Wise Weight Assessment Ratio Analysis (SWARA), is presented. A developed model allows solving complicated problems and finding a rationally, balanced solution. Arguments derived from this study help politicians and town planners as well as society.

show abstract

Grey Markov Model Prediction Method for Regular Pedestrian Movement Trend

Cited by 4 publications

References 1 publication

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Location Preferences of New Pedestrian Bridges Based on Multi-Criteria Decision-Making and GIS-Based Estimation

Contact Info

Product

Resources

About